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How can you balance data privacy and sharing with anonymization in research databases?

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Data privacy and sharing are often seen as conflicting goals in research databases, especially when sensitive or personal information is involved. However, there are ways to balance these objectives by using anonymization techniques that mask or remove identifying data without compromising the quality or usability of the data. In this article, you will learn about some of the common methods and challenges of anonymizing data, as well as some best practices and tips to ensure data privacy and sharing in research databases.

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Mehdi TAZI

Chief Technical Officer | Data & Cloud Architect | BigData & NoSQL Expert | Author of 'The Definitive Guide to Data…
Rapha?l MANSUY

Data Engineering | DataScience | AI & Innovation | Author | Follow me for deep dives on AI & data-engineering
Fernando Ferrer

Senior Data Architect/Engineer | AI Professor | Investor | #90DayFinn | Premier's Award Nominee | Mentor

1 What is anonymization?

Anonymization is the process of transforming data in a way that prevents the identification of individuals or entities from the data, either directly or indirectly. Anonymization can be applied to different types of data, such as structured, unstructured, or semi-structured data, and can be done at different levels, such as record, attribute, or value level. Anonymization can also be classified into two categories: static and dynamic. Static anonymization is when the data is anonymized before being stored or shared, while dynamic anonymization is when the data is anonymized on the fly, based on the query or access request.

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Mehdi TAZI

Chief Technical Officer | Data & Cloud Architect | BigData & NoSQL Expert | Author of 'The Definitive Guide to Data Integration' |?Founder
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Anonymization is just one kind of the tokenization technique. The tokenization is a technique for replacing sensitive data with a randomly generated token, With the difference that thethe original data is stored in a secure location like a vault. This technique is commonly used in payment processing systems to protect credit card information. Another anonymization technique is Noise Addition, which is a technique that consists of adding a random noise to the data to make it more difficult to identify individuals, as example adding a value to a salary field of a dataset, make it more difficult to identify employees based on their salary

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Rapha?l MANSUY

Data Engineering | DataScience | AI & Innovation | Author | Follow me for deep dives on AI & data-engineering
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One common anonymization technique is randomization, which involves replacing identifiable information like names or social security numbers with randomly generated identifiers. This separates the data from the individual while still maintaining the relational integrity of the data set. Generalization is another technique that entails making data less precise or specific by grouping or aggregating categories. For example, instead of listing an individual's exact age, age could be generalized into ranges like 25-30 years old. A well-anonymized data set should have low re-identification risk, which means that it is unlikely an individual could be identified by cross-referencing the anonymized data with other available information.

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2 Why is anonymization important?

Anonymization is important for several reasons. First, it can help protect the privacy and confidentiality of the data subjects, such as patients, customers, or employees, whose data is collected and used for research purposes. Anonymization can also help comply with the legal and ethical requirements of data protection and consent, such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Second, anonymization can facilitate data sharing and collaboration among researchers, institutions, or organizations, by reducing the risks of data breaches, misuse, or re-identification. Anonymization can also enable data reuse and analysis across different domains and contexts, by making the data more general and less specific.

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3 How to anonymize data?

Data anonymization is a process that involves various techniques and tools, depending on the type, level, and purpose of the data. For example, masking is when the data is replaced or obscured by random or meaningless values, such as asterisks, hashes, or blanks. Generalization is when the data is replaced by a broader or less precise value. Perturbation is when the data is modified by adding or subtracting a small amount of noise. Aggregation is when the data is grouped or summarized by a common attribute or a function. And synthesis is when the data is generated or simulated based on the original data, but without using any of the original values. All of these techniques can help protect sensitive information while still providing useful insights.

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Ridzky Pratama

IT System Analyst
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One way to anonymize data in database research is data encryption. Data encryption is the process of converting plaintext data into a secure, unreadable format called ciphertext using encryption algorithms and keys. Encryption ensures that only authorized parties with the correct decryption key can access and read the original data.

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4 What are the challenges of anonymization?

Anonymization is not an easy process and involves many challenges and trade-offs, such as data utility, re-identification, and data governance. Regarding data utility, it is essential to balance the level of anonymization with the level of data utility and consider the research questions and methods that will be applied to the data. Re-identification risk should also be evaluated and appropriate measures and safeguards should be applied to prevent or mitigate it. Finally, a clear and consistent data governance framework should be established and followed, with communication and coordination with all relevant stakeholders.

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5 How to ensure data privacy and sharing with anonymization?

Anonymization is a powerful and useful technique to balance data privacy and sharing in research databases, however it is not a one-size-fits-all solution. It requires careful planning, execution, and evaluation, based on the specific characteristics and objectives of the data and the research. To ensure data privacy and sharing with anonymization, best practices and tips include defining the purpose and scope of the data and research, choosing an appropriate anonymization technique and tool, assessing the re-identification risk and data utility of the anonymized data, implementing and monitoring a data governance framework, as well as regularly reviewing and updating the anonymization process. By following these methods, you can effectively anonymize your data without compromising your data subjects' rights and interests.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Fernando Ferrer

Senior Data Architect/Engineer | AI Professor | Investor | #90DayFinn | Premier's Award Nominee | Mentor
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There are current techniques like privacy preserving analytics where through differential privacy an encrypted algorithm can run on top of someone else's encrypted data. No data is shared, only aggregated results. Google confidential spaces and beekeper are two of those tools that work well.

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How can you balance data privacy and sharing with anonymization in research databases?

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1 What is anonymization?

2 Why is anonymization important?

3 How to anonymize data?

4 What are the challenges of anonymization?

5 How to ensure data privacy and sharing with anonymization?

6 Here’s what else to consider

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